Work

Combining biomarkers to construct a novel predictive model for predicting preoperative lymph node metastasis in early gastric cancer

Year: 2025

Type: article

Abstract: Background Accurately identifying the status of lymph node metastasis (LNM) is crucial for determining the appropriate treatment strategy for early gastric cancer (EGC) patients. Methods Univariate and multivariate logistic regression analyses were used to explore the association between clinicopathological factors and LNM in EGC patients, leading to the development of a nomogram. Differential expression analysis was conducted to identify biomarkers associated with LNM, and their expression was evaluated through immunohistochemistry. The biomarker was integrated into the conventional model to create a new model, which was then assessed for reclassification and discrimination abilities. Results Multivariate logistic regression analysis revealed that tumor size, histological type, and the presence of ulcers are independent risk factors for LNM in EGC patients. The nomogram demonstrated good clinical performance. Incorporating HAVCR1 immunohistochemical expression into the new model further improved its performance, reclassification, and discrimination abilities. Conclusion The novel nomogram predictive model, based on preoperative clinicopathological factors such as tumor size, histological type, presence of ulcers, and HAVCR1 expression, provides valuable guidance for selecting treatment strategies for EGC patients.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2025.1533889

Authors: Yujian He, Xiaoli Xie, Bingxue Yang, Xiaoxu Jin, Zhijie Feng

Institution: Second Hospital of Hebei Medical University

Language: English

Topic: Gastric Cancer Management and Outcomes

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801